diff --git a/user_data/strategies/Heracles.py b/user_data/strategies/Heracles.py
index bed3619..3eaed3d 100644
--- a/user_data/strategies/Heracles.py
+++ b/user_data/strategies/Heracles.py
@@ -8,7 +8,7 @@
 #       "min_days_listed": 100
 #   },
 # IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
-# 
+#
 # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy sell --strategy Heracles
 # ######################################################################
 # --- Do not remove these libs ---
@@ -26,44 +26,36 @@ from functools import reduce
 import numpy as np
 
 
-def normalize(df):
-    # To enable normalization outcomment below line:
-    df = (df-df.min())/(df.max()-df.min())
-    return df
-
 class Heracles(IStrategy):
     ########################################## RESULT PASTE PLACE ##########################################
-    # 35/50:    129 trades. 96/15/18 Wins/Draws/Losses. Avg profit   3.57%. Median profit   4.30%. Total profit  2302.93351920 USDT (  46.06Σ%). Avg duration 2 days, 19:04:00 min. Objective: -21.29091
-
+    # 18/100:    111 trades. 77/23/11 Wins/Draws/Losses. Avg profit   3.81%. Median profit   4.40%. Total profit  2114.06222218 USDT (  42.28Σ%). Avg duration 3 days, 3:04:00 min. Objective: -16.78579
 
     # Buy hyperspace params:
     buy_params = {
-        "buy_crossed_indicator_shift": -5,
-        "buy_div": 4.7968,
-        "buy_indicator_shift": 5,
+        "buy_crossed_indicator_shift": 5,
+        "buy_div": 3.61,
+        "buy_indicator_shift": 1,
     }
 
     # Sell hyperspace params:
     sell_params = {
-        "sell_atol": 0.21256,
-        "sell_crossed_indicator_shift": 0,
-        "sell_indicator_shift": -1,
-        "sell_rtol": 0.11195,
+        "sell_atol": 0.30989,
+        "sell_crossed_indicator_shift": 2,
+        "sell_indicator_shift": 5,
+        "sell_rtol": 0.19449,
     }
 
     # ROI table:
     minimal_roi = {
-        "0": 0.43,
-        "994": 0.076,
-        "2864": 0.043,
-        "6947": 0
+        "0": 0.725,
+        "889": 0.171,
+        "2776": 0.044,
+        "5299": 0
     }
-    
     # Stoploss:
     stoploss = -0.312
-    
-    ########################################## END RESULT PASTE PLACE ######################################
 
+    ########################################## END RESULT PASTE PLACE ######################################
 
     # buy params
     buy_div = DecimalParameter(-5, 5, default=0.51844, decimals=4, space='buy')
@@ -71,51 +63,47 @@ class Heracles(IStrategy):
     buy_crossed_indicator_shift = IntParameter(-5, 5, default=1, space='buy')
 
     # sell params
-    sell_rtol = DecimalParameter(1.e-10, 1.e-0, default=0.05468, decimals=4, space='sell')
-    sell_atol = DecimalParameter(1.e-16, 1.e-0, default=0.00019, decimals=4, space='sell')
+    sell_rtol = DecimalParameter(1.e-10, 1.e-0, default=0.05468, decimals=10, space='sell')
+    sell_atol = DecimalParameter(1.e-16, 1.e-0, default=0.00019, decimals=10, space='sell')
     sell_indicator_shift = IntParameter(-5, 5, default=4, space='sell')
     sell_crossed_indicator_shift = IntParameter(-5, 5, default=1, space='sell')
 
-
     # Optimal timeframe use it in your config
     timeframe = '4h'
 
-  
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe = dropna(dataframe)
 
-        dataframe['volatility_kcw'] = normalize(ta.volatility.keltner_channel_wband(
-                dataframe['high'],
-                dataframe['low'],
-                dataframe['close'],
-                window=20,
-                window_atr=10,
-                fillna=False,
-                original_version=True
-            ))
-        
-        dataframe['volatility_dcp'] =normalize(ta.volatility.donchian_channel_pband(
-                dataframe['high'],
-                dataframe['low'],
-                dataframe['close'],
-                window=10,
-                offset=0,
-                fillna=False
-            ))
-    
-        dataframe['trend_macd_signal'] =normalize(ta.trend.macd_signal(
-                    dataframe['close'],
-                    window_slow=26,
-                    window_fast=12,
-                    window_sign=9,
-                    fillna=False
-            ))
-        
+        dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=20,
+            window_atr=10,
+            fillna=False,
+            original_version=True
+        )
 
-        dataframe['trend_ema_fast'] =normalize(ta.trend.EMAIndicator(
-                close=dataframe['close'], window=12, fillna=False
-            ).ema_indicator())
-        
+        dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=10,
+            offset=0,
+            fillna=False
+        )
+
+        dataframe['trend_macd_signal'] = ta.trend.macd_signal(
+            dataframe['close'],
+            window_slow=26,
+            window_fast=12,
+            window_sign=9,
+            fillna=False
+        )
+
+        dataframe['trend_ema_fast'] = ta.trend.EMAIndicator(
+            close=dataframe['close'], window=12, fillna=False
+        ).ema_indicator()
 
         # for checking crossovers!
         # but we dont need to crossovers we just calculate dividation